Peritoneal Dialysis–Associated Peritonitis: Suggestions for Management and Mistakes to Avoid
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Peritonitis is a common complication of peritoneal dialysis that is associated with substantial morbidity and mortality. Peritonitis increases treatment costs and hospitalization events and is the most common reason for transfer to hemodialysis. Although there is much focus on preventing peritoneal dialysis-associated peritonitis, equally as important is appropriate management to minimize the morbidity of a peritonitis episode when it has occurred. Despite the presence of international guidelines on peritonitis treatment, the evidence base to support optimal peritonitis treatment practices is lacking, leaving the practitioner to rely on clinical experience and extrapolate from across other infection treatment practices. This article reviews common mistakes and misconceptions that we have observed in the management of peritonitis that may compromise treatment success. It also provides suggestions on common controversial aspects of peritonitis management based on the best available literature. Although the use of the word mistakes is somewhat controversial and subjective, we acknowledge that evidence is lacking and have based many of our suggestions on clinical judgment, experience, and available data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it